Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use scales-okn/ontology-arbitration-motion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scales-okn/ontology-arbitration-motion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scales-okn/ontology-arbitration-motion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scales-okn/ontology-arbitration-motion") model = AutoModelForSequenceClassification.from_pretrained("scales-okn/ontology-arbitration-motion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d2f5ad16c964d3bb9ba5fda4b0b5147000497275eb31596e635f0ca47469a44
- Size of remote file:
- 3.25 kB
- SHA256:
- 2b29d733a46db8b17a1b784e247f65e88a9c28024abf1466d6f60c60ab16f481
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